Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Anasua Kundu; Michael Chaiton; Rebecca Billington; Daniel Grace; Rui Fu; Carmen Logie; Bruce Baskerville; Christina Yager; Nicholas Mitsakakis; Robert Schwartz

doi:10.2196/28962

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

JMIR Med Inform. 2021 Nov 11;9(11):e28962. doi: 10.2196/28962.

Authors

Anasua Kundu¹, Michael Chaiton^{1

2}, Rebecca Billington³, Daniel Grace², Rui Fu⁴, Carmen Logie^{3

5}, Bruce Baskerville^{6

7}, Christina Yager¹, Nicholas Mitsakakis^{2

8}, Robert Schwartz^{1

2

4}

Affiliations

¹ Centre for Addiction and Mental Health, Toronto, ON, Canada.
² Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
³ Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.
⁴ Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
⁵ Women's College Research Institute, Toronto, ON, Canada.
⁶ Canadian Institutes of Health Research, Government of Canada, Ottawa, ON, Canada.
⁷ School of Pharmacy, Faculty of Science, University of Waterloo, Kitchener, ON, Canada.
⁸ Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada.

PMID: 34762059
PMCID: PMC8663464
DOI: 10.2196/28962

Abstract

Background: A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods.

Objective: This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field.

Methods: The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings.

Results: We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%).

Conclusions: ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.

Keywords: machine learning; mental disorders; mental health; sexual and gender minorities; substance-related disorders.

©Anasua Kundu, Michael Chaiton, Rebecca Billington, Daniel Grace, Rui Fu, Carmen Logie, Bruce Baskerville, Christina Yager, Nicholas Mitsakakis, Robert Schwartz. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 11.11.2021.

Publication types

Review